{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we'll walk through the basics of preparing your data for use with SDV. This includes loading your dataset and creating a metadata description for your tables. You can download the files used in this tutorial from the following link:\n",
        "https://drive.google.com/drive/folders/1WRcrmDT_S9xq9CpqzD7WxSpWbLkwadAe?usp=sharing"
      ],
      "metadata": {
        "id": "6uyUZ0zdv8Pn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Installing the dependencies\n",
        "We will first install the ***sdv*** library."
      ],
      "metadata": {
        "id": "ToH5dWaMs-fK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "L2VyoP-6bL70",
        "outputId": "9132555d-16cd-4885-d6aa-727e3dd2f3e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, jmespath, Faker, nvidia-cusparse-cu12, nvidia-cudnn-cu12, botocore, s3transfer, rdt, nvidia-cusolver-cu12, copulas, sdmetrics, boto3, deepecho, ctgan, sdv\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed Faker-37.3.0 boto3-1.38.23 botocore-1.38.23 copulas-0.12.2 ctgan-0.11.0 deepecho-0.7.0 jmespath-1.0.1 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 rdt-1.17.0 s3transfer-0.13.0 sdmetrics-0.20.1 sdv-1.21.0\n"
          ]
        }
      ],
      "source": [
        "!pip install sdv"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Loading the CSV file\n",
        "Upload the CSV file for which you want to generate synthetic data. In this tutorial, we’ll use a sample sales dataset, but feel free to upload your own file instead."
      ],
      "metadata": {
        "id": "0X0OnZrKtUHd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 Upload your CSV file"
      ],
      "metadata": {
        "id": "XcYxZHKwtxbS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "id": "U6murAS8cZWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.2 Loading the data in Python"
      ],
      "metadata": {
        "id": "1PQC0V5Ft4Fz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sdv.io.local import CSVHandler\n",
        "\n",
        "connector = CSVHandler()\n",
        "FOLDER_NAME = '/content/'\n",
        "\n",
        "data = connector.read(folder_name=FOLDER_NAME)"
      ],
      "metadata": {
        "id": "JEgWaAvZrQil"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.3 Inspecting the data"
      ],
      "metadata": {
        "id": "kAgqQ9Vyt66w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MDlvO-9rbb5",
        "outputId": "ecfa7b10-1cf2-4465-c709-dba95d3f6595"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data'])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "salesDf = data['data']\n",
        "salesDf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "s-F8jafSrgUl",
        "outputId": "fc8f1384-f2a5-477c-fd9c-b6efb2174d0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Transaction ID        Date Country         Manufacturer  \\\n",
              "0        T000001  01-01-2024     USA    Aether Industries   \n",
              "1        T000002  01-01-2024     USA    Aether Industries   \n",
              "2        T000003  01-01-2024     USA    Aether Industries   \n",
              "3        T000004  01-01-2024     USA    Aether Industries   \n",
              "4        T000005  01-01-2024     USA  Radiant Innovations   \n",
              "\n",
              "                  Product        Unit     Sales       Channel  \n",
              "0         Rainbow Unicorn   30.453271   3689.97     Wholesale  \n",
              "1  Aetheron Mountain Bike  103.302272  15285.84     Wholesale  \n",
              "2             Lux Cruiser   59.115173  18424.82  Direct Sales  \n",
              "3         Outdoor Speaker   71.057632  13146.93     Wholesale  \n",
              "4          Gaming Headset  288.055057  20054.01  Direct Sales  "
            ],
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              "type": "dataframe",
              "variable_name": "salesDf",
              "summary": "{\n  \"name\": \"salesDf\",\n  \"rows\": 5889,\n  \"fields\": [\n    {\n      \"column\": \"Transaction ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5889,\n        \"samples\": [\n          \"T005378\",\n          \"T004335\",\n          \"T000200\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 366,\n        \"samples\": [\n          \"12-07-2024\",\n          \"03-02-2024\",\n          \"16-01-2024\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Manufacturer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Radiant Innovations\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"Star Road Bike\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1234.010141859866,\n        \"min\": 29.85614804,\n        \"max\": 5209.233317,\n        \"num_unique_values\": 3012,\n        \"samples\": [\n          3931.693615\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43421.06690866911,\n        \"min\": 680.53,\n        \"max\": 198714.16,\n        \"num_unique_values\": 5888,\n        \"samples\": [\n          4844.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Direct Sales\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Loading the Metadata file\n",
        "We will now upload the metadata file for the CSV file we uploaded."
      ],
      "metadata": {
        "id": "ssyaHsnMuAMk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 Upload your metadata.json file"
      ],
      "metadata": {
        "id": "iHm1XztYuIO0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "id": "yuVfplC6rqfM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 Loading the metadata into Python"
      ],
      "metadata": {
        "id": "YmCxcaamuLxA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sdv.metadata import Metadata\n",
        "metadata = Metadata.load_from_json('metadata.json')"
      ],
      "metadata": {
        "id": "Zxn3UMUjrmbV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 (Optional) Creating the metadata using SDV\n",
        "Alternatively, we can use the SDV library to automatically infer the metadata. However, the results may not always be accurate or complete, so you might need to review and update it if there are any discrepancies."
      ],
      "metadata": {
        "id": "FNHtN3WXuamp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sdv.metadata import Metadata\n",
        "\n",
        "metadata = Metadata.detect_from_dataframes(data)"
      ],
      "metadata": {
        "id": "oXGJBR6wugs_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Auto detected data:\\n')\n",
        "metadata.visualize()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "vQ1Dm7g5ukHK",
        "outputId": "0b0c804a-ac44-4498-9aa5-1683bd3b326e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Auto detected data:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Metadata Pages: 1 -->\n<svg width=\"181pt\" height=\"183pt\"\n viewBox=\"0.00 0.00 181.00 183.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 179)\">\n<title>Metadata</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-179 177,-179 177,4 -4,4\"/>\n<!-- data -->\n<g id=\"node1\" class=\"node\">\n<title>data</title>\n<path fill=\"#ffec8b\" stroke=\"black\" d=\"M12,-0.5C12,-0.5 161,-0.5 161,-0.5 167,-0.5 173,-6.5 173,-12.5 173,-12.5 173,-162.5 173,-162.5 173,-168.5 167,-174.5 161,-174.5 161,-174.5 12,-174.5 12,-174.5 6,-174.5 0,-168.5 0,-162.5 0,-162.5 0,-12.5 0,-12.5 0,-6.5 6,-0.5 12,-0.5\"/>\n<text text-anchor=\"middle\" x=\"86.5\" y=\"-159.3\" font-family=\"Times,serif\" font-size=\"14.00\">data</text>\n<polyline fill=\"none\" stroke=\"black\" points=\"0,-151.5 173,-151.5 \"/>\n<text text-anchor=\"start\" x=\"8\" y=\"-136.3\" font-family=\"Times,serif\" font-size=\"14.00\">Transaction ID : id</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-121.3\" font-family=\"Times,serif\" font-size=\"14.00\">Date : categorical</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\">Country : categorical</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\">Manufacturer : categorical</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\">Product : categorical</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\">Unit : numerical</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\">Sales : numerical</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-31.3\" font-family=\"Times,serif\" font-size=\"14.00\">Channel : categorical</text>\n<polyline fill=\"none\" stroke=\"black\" points=\"0,-23.5 173,-23.5 \"/>\n<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\">Primary key: Transaction ID</text>\n</g>\n</g>\n</svg>\n",
            "text/plain": [
              "<graphviz.graphs.Digraph at 0x7f8d4a2b1850>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.4 Validating the metadata\n",
        "Let's validate that the metadata format makes sense. If successful, the code below should run without any errors."
      ],
      "metadata": {
        "id": "aVOLXTG1vBr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metadata.validate()"
      ],
      "metadata": {
        "id": "q3CDMvqtsMb0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Creating Synthetic Data\n",
        "With these preparatory steps complete, we can now use the metadata and original dataset with SDV. The code below trains a model and generates synthetic data."
      ],
      "metadata": {
        "id": "o4XB_ZwEvLTr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sdv.single_table import GaussianCopulaSynthesizer\n",
        "\n",
        "synthesizer = GaussianCopulaSynthesizer(metadata)\n",
        "synthesizer.fit(data=salesDf)"
      ],
      "metadata": {
        "id": "fmc-eWIWsAkg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can specify the number of rows you want the synthesizer to generate using the *num_rows* argument\n"
      ],
      "metadata": {
        "id": "DaXwL5GmvZgD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_data = synthesizer.sample(num_rows=10000)"
      ],
      "metadata": {
        "id": "hsTDD9upsD_a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.1 Evaluating the synthetic data"
      ],
      "metadata": {
        "id": "9JE6CwHrviJr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sdv.evaluation.single_table import evaluate_quality\n",
        "\n",
        "quality_report = evaluate_quality(\n",
        "    salesDf,\n",
        "    synthetic_data,\n",
        "    metadata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HhGC_ibsNYy",
        "outputId": "a3bba728-3a76-4b02-8bc2-8655c6330a2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating report ...\n",
            "\n",
            "(1/2) Evaluating Column Shapes: |██████████| 8/8 [00:00<00:00, 49.86it/s]|\n",
            "Column Shapes Score: 97.67%\n",
            "\n",
            "(2/2) Evaluating Column Pair Trends: |██████████| 28/28 [00:00<00:00, 76.39it/s]|\n",
            "Column Pair Trends Score: 89.68%\n",
            "\n",
            "Overall Score (Average): 93.67%\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.2 Visualizing the synthetic vs. real data distribution"
      ],
      "metadata": {
        "id": "J1aE1er5vmIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sdv.evaluation.single_table import get_column_plot\n",
        "\n",
        "fig = get_column_plot(\n",
        "    real_data=salesDf,\n",
        "    synthetic_data=synthetic_data,\n",
        "    column_name='Sales',\n",
        "    metadata=metadata\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "TrzP-OPKsSp4",
        "outputId": "575503e8-8d52-4d16-ae70-fedfae3e4649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "source": [
        "## 4.3 Using Matplotlib to see the Average Monthly Sales for Real & Synthetic Data"
      ],
      "metadata": {
        "id": "nbKnd42Ovst4"
      }
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Ensure 'Date' columns are datetime\n",
        "salesDf['Date'] = pd.to_datetime(salesDf['Date'], format='%d-%m-%Y')\n",
        "synthetic_data['Date'] = pd.to_datetime(synthetic_data['Date'], format='%d-%m-%Y')\n",
        "\n",
        "# Extract 'Month' as year-month string\n",
        "salesDf['Month'] = salesDf['Date'].dt.to_period('M').astype(str)\n",
        "synthetic_data['Month'] = synthetic_data['Date'].dt.to_period('M').astype(str)\n",
        "\n",
        "# Group by 'Month' and calculate average sales\n",
        "actual_avg_monthly = salesDf.groupby('Month')['Sales'].mean().rename('Actual Average Sales')\n",
        "synthetic_avg_monthly = synthetic_data.groupby('Month')['Sales'].mean().rename('Synthetic Average Sales')\n",
        "\n",
        "# Merge the two series into a DataFrame\n",
        "avg_monthly_comparison = pd.concat([actual_avg_monthly, synthetic_avg_monthly], axis=1).fillna(0)\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Actual Average Sales'], label='Actual Average Sales', marker='o')\n",
        "plt.plot(avg_monthly_comparison.index, avg_monthly_comparison['Synthetic Average Sales'], label='Synthetic Average Sales', marker='o')\n",
        "\n",
        "plt.title('Average Monthly Sales Comparison: Actual vs Synthetic')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Average Sales')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.ylim(bottom=0)  # y-axis starts at 0\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
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      "execution_count": null,
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}